A new approach based on orthogonal bases of data space to decomposition of mixed pixels for hyperspectral imagery

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.

Recent advances in hyperspectral image processing

Hyperspectral images(HSI)provide a new way to exploit the internal physical composition of the land scene.The basic platform for acquiring HSI data-sets are airborne or spaceborne spectral imaging.Retrieving useful information from hyperspectral images can be grouped into four categories.(1)Classification:Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task.(2)Endmember extraction and spectral unmixing:Among images,only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the elements.Identification of endmembers from HSI thus becomes the foremost step in interpretation of each pixel.With proper endmembers,the corresponding abundances can also be exactly calculated.(3)Target detection:Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex background.Constructing a reliable rule for separating target signals from all the other background signals,even in the case of low target occurrence and high spectral variation,comprises the key to this problem.(4)Change detection:Although change detection is not a new problem,detecting changes from hyperspectral images has brought new challenges,since the spectral bands are so many,accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in HSI.In this paper,the basic theory and the most canonical works are discussed,along with the most recent advances in each aspect of hyperspectral image processing.